Integrating protein structure and genomic data to predict antibiotic resistance in Mycobacterium tuberculosis
整合蛋白质结构和基因组数据来预测结核分枝杆菌的抗生素耐药性
基本信息
- 批准号:10312207
- 负责人:
- 金额:$ 6.64万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-07-15 至 2023-10-02
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalActive SitesAddressAffectAntibiotic ResistanceAntibioticsAntitubercular AgentsAntitubercular AntibioticsBacteriaBacterial GenesBenchmarkingBiologicalCessation of lifeClinicClinicalComputer AnalysisConsumptionCrystallizationDataData SetDevelopmentDiagnosisDiagnosticDiseaseEvolutionFrequenciesGenesGenetic MarkersGenomeGenomicsGenotypeKnowledgeLeadLigand BindingLocationM. tuberculosis genomeMapsMeasuresMethodsMicrobeModelingMolecularMutationMycobacterium tuberculosisPharmaceutical PreparationsPhenotypePrevalenceProteinsProteomeResistanceShapesSignal TransductionSiteSourceStatistical MethodsStructureSumTestingTimeTuberculosisVariantWorkbasefitnessgenetic associationgenetic variantgenomic datahuman pathogenmicrobial genomepathogenpathogenic bacteriaphenotypic dataprotein functionprotein structureprotein structure predictionresistant strainthree dimensional structuretool
项目摘要
Project Abstract
Tuberculosis causes over one million deaths annually, and increasing antibiotic resistance is rendering
the disease more difficult to treat. Rapid genotype-based resistance diagnosis of Mycobacterium tuberculosis,
the bacterium that causes tuberculosis, is needed to overcome the long treatment delays associated with culture-
based methods. Previous work has established sets of genetic markers of antibiotic resistance to more common
antibiotics, but such studies require large numbers of sequenced resistant isolates, and are unable to make
predictions for rare or newly observed variants. The requirement for large numbers of isolates is especially
problematic for five newly introduced antitubercular agents, which have small but increasing numbers of
documented resistant isolates.
Traditional methods for associating genotype with phenotype assume that every site is independent, and
therefore many examples of mutations at a particular site are needed to infer statistically significant effects of
variants on phenotype. Biological knowledge tells us that this assumption is not true – most bacterial genes
encode proteins, which have distinct three-dimensional shapes and functions. Mutations that causes changes in
similar regions of a protein are more likely to have similar effects on phenotype, potentially allowing for sharing
of statistical signal that could increase the power of significance testing.
In this proposed project, I will develop two complimentary statistical approaches that will use protein
three-dimensional structure to boost signal from genetic variants that cause antibiotic resistance in M.
tuberculosis. Specifically, I will first develop an unsupervised statistical test to determine if repeated mutations
within the same protein are clustered in three-dimensional space, which indicates that the mutations confer a
fitness benefit. This approach will have increased sensitivity over traditional methods that look for significant
numbers of mutations, and facilitate the development of mechanistic hypotheses about the effects of mutation
on protein function. Second, I will use protein three-dimensional structure as a prior in a Bayesian linear mixed
model to predict antibiotic resistance. This prior will allow nearby variants to ‘boost’ one another’s signal and
establish associations between genotype and phenotype that are beyond the reach of current methods. The key
application of this approach will be establishing resistance-conferring genotypes for five newly introduced
antitubercular agents. The approach proposed here will likely generalize to other bacterial pathogens and
represent an important leap forward in using pathogen molecular data in the clinic.
项目摘要
结核病每年导致超过100万人死亡,并且抗生素耐药性增加正在引起
该疾病更难治疗。基于基因型的快速抗性结核分枝杆菌的抗性诊断,
需要引起结核病的细菌来克服与培养的长期治疗延迟 -
基于方法。先前的工作已经建立了对更常见的抗生素耐药性的遗传标记
抗生素,但是这样的研究需要大量测序的抗性分离株,并且无法制作
对稀有或新观察到的变体的预测。尤其是对大量分离株的要求
对于五种新引入的抗结核剂而言,有问题,它们的数量较小但越来越多
记录的抗性分离株。
将基因型与表型关联的传统方法假设每个站点都是独立的,并且
因此,需要在特定位点进行许多突变的例子,以推断出统计学上的显着影响
表型上的变体。生物学知识告诉我们,这个假设不是真实的 - 大多数细菌基因
编码具有不同三维形状和功能的蛋白质。导致变化的突变
类似的蛋白质区域更有可能对表型产生相似的影响,可能允许共享
统计信号可能会增加显着性测试的能力。
在这个拟议的项目中,我将开发两种免费的统计方法,这些方法将使用蛋白质
三维结构可从遗传变异体中提高信号。
结核病。特别是,我将首先开发无监督的统计检验,以确定是否重复突变
在同一蛋白质中,聚集在三维空间中,这表明突变会议A
健身益处。这种方法将对寻求重要的传统方法具有更高的敏感性
突变数量,并支持有关突变影响的机械假设的发展
关于蛋白质功能。其次,我将使用蛋白质三维结构作为贝叶斯线性混合的先验
预测抗生素耐药性的模型。此先验将允许附近的变体“增强”彼此的信号和
在基因型和表型之间建立关联,这些结合超出了当前方法的范围。钥匙
这种方法的应用将建立五个新引入的抗性基因型
抗结核剂。这里提出的方法可能会推广到其他细菌病原体和
在使用病原体分子数据中,代表着重要的飞跃。
项目成果
期刊论文数量(0)
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